Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data

Yang, Haoqing; Zhang, Lulu; Pan, Qiujing; Phoon, Kok‐Kwang; Shen, Zuowei

doi:10.1007/s11440-020-00991-z

Cited by 51 publications

(15 citation statements)

References 68 publications

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“…The application of this machine learning algorithm significantly reduced the direct slope stability evaluation in the probabilistic analysis. Yang et al 66 . proposed a procedure for the characterization of spatially variable soil properties in an unsaturated slope using a Bayesian inverse method combined with a surrogate model based on adaptive sparse polynomial chaos expansion.…”

Section: Discussionmentioning

confidence: 99%

Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression‐based surrogate model

Zhu

Hiraishi

Pei

et al. 2020

Num Anal Meth Geomechanics

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Efficient evaluation of slope stability is a frontier in geo‐disaster prevention fields. While many slope stability evaluation methods, ranging from deterministic to probabilistic, have been proposed, reliability methods are particularly advantageous as they can account for uncertainties during slope stability evaluation. To address the problem of the low efficiency of direct Monte Carlo simulations and overcome the defects of the traditional response surface method, in this work, a novel probabilistic procedure that integrates a Gaussian process regression‐based surrogate model and the random limit equilibrium method for slope stability evaluation while accounting for the spatial variability of soil strength is proposed. The novel Gaussian process regression‐based surrogate model is efficiently used in the Monte Carlo simulation to reduce the number of calls for direct stability analysis of a slope with spatially varied soil strength. To verify the accuracy and efficiency of the proposed procedure, it was applied to three case studies: the first one is a slope in saturated clay under undrained conditions; the second one is a slope for which the friction angle and cohesion values are cross‐correlated; and the third one is a real slope with multiple soil layers. The results obtained from the comparisons with other methods confirmed the precision and feasibility of the proposed procedure.

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Section: Discussionmentioning

confidence: 99%

Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression‐based surrogate model

Zhu

Hiraishi

Pei

et al. 2020

Num Anal Meth Geomechanics

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“…Figure 3B shows the results of C u estimated based on SPTN, according to Decourt (1990). Number of cohesion values used at various depths vary, for example at depths of (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25) The collected data were presented in histograms to be more reliable and to provide a means to reduce the high uncertainty. It is noted in Figure 4 that 95% of the cohesion rate in most projects ranges from (50-150) kPa, which was estimated through the equations of the corrected values for SPT N according to Decourt [17].…”

Section: Previous Knowledgementioning

confidence: 99%

“…When combined with other geotechnical metrics, such as test results from in-field and laboratory experiments and field monitoring data, Bayesian techniques offer a potent paradigm for evaluating the site conditions. By combining field and lab test data, the Bayesian technique allows for the probabilistic calculation of soil properties [19]. The data and statistical parameters are both treated as random variables.…”

Section: Bayesian Approachmentioning

confidence: 99%

Uncertainty Characterization for Soil Cohesion in a Project Site in Nasiriyah Using Bayesian Methods

Abd Al-Haleem,

Shakir

2023

E3S Web Conf.

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High uncertainties arias through the characterization of soil parameters because of the lack of data obtained from geotechnical reports. Reducing these uncertainties may improve the characteristic values of soil parameters. This research aims to probabilistically characterize a soil's cohesion parameter in Nasiriyah. The Bayesian approach has been applied to soil data obtained through a project in Nasiriyah. The soil at the site is classified as lean clay, and the soil cohesion has been evaluated using two Bayesian methods: the ordinary, normal distribution method (OND) and the Marcove Chain Monte Carlo-based Bayesian approach (MCMC) method. The previous knowledge utilized in the Bayesian approach was based on 20 boreholes, and the subjective probability approach has functioned in the prior probability distribution. The OND method deduced a mean value of cohesion of (195.9 kPa) and a standard deviation of (14.68 kPa), (COV) 7.49%. It was noted that the probability distribution has a more significant effect than the previous distribution on the posterior distribution. The MCMC method summarized the probabilistic description of the soil characteristic, through which it reached the mean and the subsequent standard deviation (167.49) kPa (109.8) kPa, respectively, and the coefficient of Variation (COV) was 65.6%. It is considered the most appropriate and common method, especially in high-dimensional data when the results are not well known because it can provide a probabilistic value for the not well-known data.

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“…In addition, some scholars have applied the Bayesian method to spatially varying parameter inverse analyses [28,29]. On the basis of the estimation results of parameter fields, the deformation field can be simulated.…”

Section: Introductionmentioning

confidence: 99%

Ordinary Kriging Interpolation Method Combined with FEM for Arch Dam Deformation Field Estimation

Shao

Chen

et al. 2023

Mathematics

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The deformation characteristic of the arch dam can directly reflect its service performance, which can be analyzed on the basis of the dam deformation field. However, restricted by the limited number of dam monitoring points and the inhomogeneity of materials, an accurate measurement of arch dam deformation field is difficult to estimate by using the existing common methods, such as the spatial interpolation methods and the finite element method (FEM). With the aim of ensuring arch dam structure safety, the ordinary kriging interpolation method, combined with FEM, is proposed for arch dam deformation field estimation, in this study. Given the inversion of the computation parameters of the arch dam, FEM is used to calculate the basic arch dam deformation. Subsequently, the ordinary kriging interpolation method is introduced to estimate the spatial variance deformation at each point of the arch dam. One superhigh arch dam in China is selected as a case study; two additional methods are introduced as comparisons that are based on a numerical experiment and the actual monitoring data. The experimental results show that the proposed method considerably improves the accuracy and computational efficiency of the arch dam deformation field estimation and that it is of great practical importance for characterizing the deformation behavior of arch dams.

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Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data

Cited by 51 publications

References 68 publications

Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression‐based surrogate model

Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression‐based surrogate model

Uncertainty Characterization for Soil Cohesion in a Project Site in Nasiriyah Using Bayesian Methods

Ordinary Kriging Interpolation Method Combined with FEM for Arch Dam Deformation Field Estimation

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